Space-Filling Designs for Robustness Experiments |
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Authors: | V. Roshan Joseph Li Gu Shan Ba William R. Myers |
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Affiliation: | 1. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA;2. Amazon.com, Inc., Seattle, WA;3. Quantitative Sciences, The Procter &4. Gamble Company, Mason, OH |
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Abstract: | To identify the robust settings of the control factors, it is very important to understand how they interact with the noise factors. In this article, we propose space-filling designs for computer experiments that are more capable of accurately estimating the control-by-noise interactions. Moreover, the existing space-filling designs focus on uniformly distributing the points in the design space, which are not suitable for noise factors because they usually follow nonuniform distributions such as normal distribution. This would suggest placing more points in the regions with high probability mass. However, noise factors also tend to have a smooth relationship with the response and therefore, placing more points toward the tails of the distribution is also useful for accurately estimating the relationship. These two opposing effects make the experimental design methodology a challenging problem. We propose optimal and computationally efficient solutions to this problem and demonstrate their advantages using simulated examples and a real industry example involving a manufacturing packing line. Supplementary materials for the article are available online. |
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Keywords: | Computer experiments Experimental design Gaussian process Optimal designs Quality improvement Robust parameter design |
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